Tidy Text Notebook

By David Robinson and Julia Silge

This is my notebook that I will used to go through the tidy text notebook by Julia Silge and David Robinson.

library(tidyverse)
library(tidytext)
library(stringr)
library(janeaustenr)
text <- c("Because I could not stop for Death - ", 
          "He kindly stopped for me -",
          "The Carriage held but just Ourselves -",
          "and Immortality")
text

[1] "Because I could not stop for Death - "  "He kindly stopped for me -"             "The Carriage held but just Ourselves -" "and Immortality"                       

text_df <- data_frame(line = 1:4, text = text)
text_df

text_df %>% 
  unnest_tokens(word, text)  # to_lower = FALSE to keep uppercase

Working with Jane Austen Books

Using the Jane Austen dataset we can make it tidy. First we’ll use mutate to create a column from existing data. Annotate line numbers and then keep track of chapters using regex.

original_books <- austen_books() %>% 
  group_by(book) %>% 
  mutate(linenumber = row_number(),
         chapter = cumsum(str_detect(text, regex("^chapter [\\divxlc]",
                                                  ignore.case = TRUE)))) %>% 
  ungroup()
original_books

Restructure it in one-token-per-row format with the unnest_tokens() function.

tidy_books <- original_books %>% 
  unnest_tokens(word, text)
tidy_books

Removing stop words

We can remove stop words

data(stop_words)
tidy_books <- tidy_books %>% 
  anti_join(stop_words)

Joining, by = "word"

tidy_books %>% 
  count(word, sort = TRUE)

Plotting data

tidy_books %>% 
  count(word, sort = TRUE) %>% 
  filter(n > 600) %>% 
  mutate(word = reorder(word, n)) %>% 
  ggplot(aes(word, n))+
  geom_col()+
  xlab(NULL)+
  coord_flip()+
  theme_bw()

Gutenbergr

HG wells books

library(gutenbergr)
hgwells <-  gutenberg_download(c(35, 36, 5230, 159))

Determining mirror for Project Gutenberg from http://www.gutenberg.org/robot/harvest
Using mirror http://aleph.gutenberg.org

tidy_hgwells <- hgwells %>% 
  unnest_tokens(word, text) %>% 
  anti_join(stop_words)

Joining, by = "word"

tidy_hgwells %>% 
  count(word, sort = TRUE)

Most commone words in novels by Bronte sisters

bronte <-  gutenberg_download(c(1260, 768, 969, 9182, 767))
tidy_bronte <- bronte %>% 
  unnest_tokens(word, text) %>% 
  anti_join(stop_words)

Joining, by = "word"

tidy_bronte %>% 
  count(word, sort = TRUE)

Comparing Bronte and HG Wells

frequency <- bind_rows(mutate(tidy_bronte, author = "Bronte Sisters"),
                       mutate(tidy_hgwells, author = "H.G. Wells"),
                       mutate(tidy_books, author = "Jane Austen")) %>% 
  mutate(word = str_extract(word, "[a-z']+")) %>% 
  count(author, word) %>% 
  group_by(author) %>% 
  mutate(proportion = n / sum(n)) %>% 
  select(-n) %>% 
  spread(author, proportion) %>% 
  gather(author, proportion, `Bronte Sisters`:`H.G. Wells`)

Graphing the comparisons

library(scales)

Attaching package: ‘scales’

The following object is masked from ‘package:purrr’:

    discard

The following object is masked from ‘package:readr’:

    col_factor

ggplot(frequency, aes(x = proportion, y = `Jane Austen`,
                      color = abs(`Jane Austen` - proportion))) +
  geom_abline(color = "gray40", lty = 2) +
  geom_jitter(alpha = 0.1, size = 2.5, width = 0.3, height = 0.3) +
  geom_text(aes(label = word, check_overlap = TRUE, vjust = 1.5))+
  scale_x_log10(labels = percent_format()) +
  scale_y_log10(labels = percent_format()) +
  scale_color_gradient(limits = c(0,0.001),
                       low = "darkslategray4", high = "gray75") +
  facet_wrap(~author, ncol = 2) +
  theme(legend.position = "none") +
  labs(y = "Jane Austen", x = NULL)

Ignoring unknown aesthetics: check_overlap

NA

Correlation analysis

cor.test(data = frequency[frequency$author == "Bronte Sisters",],
         ~ proportion + `Jane Austen`)

    Pearson's product-moment correlation

data:  proportion and Jane Austen
t = 119.65, df = 10404, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.7527846 0.7689620
sample estimates:
      cor 
0.7609915 

cor.test(data = frequency[frequency$author == "H.G. Wells",],
         ~ proportion + `Jane Austen`)

    Pearson's product-moment correlation

data:  proportion and Jane Austen
t = 36.441, df = 6053, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.4032800 0.4445987
sample estimates:
      cor 
0.4241601 

Sentiments dataset

library(tidytext)
sentiments

Sentiment scores

# AFINN
get_sentiments("afinn")

# Bing
get_sentiments("bing")

# NRC
get_sentiments("nrc")

Sentiment analysis can be performed with an inner join

tidy_books <- austen_books() %>% 
  group_by(book) %>% 
  mutate(linenumber = row_number(),
         chapter = cumsum(str_detect(text, regex("^chapter [\\divxlc]",
                                                 ignore_case = TRUE)))) %>% 
  ungroup() %>% 
  unnest_tokens(word, text)
tidy_books

Get a list of words that are associated with ‘joy’ and perform an inner_join to find words that are have ‘joy’ sentiment within Jane Austen’s Emma book.

# Filter words associated with 'joy'
nrcjoy <- get_sentiments("nrc") %>% 
  filter(sentiment == "joy")
# Find words associated with joy in Emma
tidy_books %>% 
  filter(book == "Emma") %>% 
  inner_join(nrcjoy) %>% 
  count(word, sort = TRUE)

Joining, by = "word"

Count up positive or negative words that are within sections of each book. We will define an index to keep track of where we are in the narrative. The index will count up sections of 80 lines of text

janeaustensentiment <- tidy_books %>% 
  inner_join(get_sentiments("bing")) %>% 
  count(book, index = linenumber %/% 80, sentiment) %>% 
  spread(sentiment, n, fill =0) %>% 
  mutate(sentiment = positive - negative)

Joining, by = "word"

janeaustensentiment

ggplot(janeaustensentiment, aes(index, sentiment, fill = book)) +
  geom_col(show.legend = FALSE) +
  facet_wrap(~book, ncol = 2, scales = "free_x")

pride_prejudice <- tidy_books %>% 
  filter(book == "Pride & Prejudice")
pride_prejudice

afinn <- pride_prejudice %>% 
  inner_join(get_sentiments("afinn")) %>% 
  group_by(index = linenumber %/% 80) %>% 
  summarise(sentiment = sum(score)) %>% 
  mutate(method = "AFINN")

Joining, by = "word"

afinn

bing_and_nrc <- bind_rows(
  pride_prejudice %>% 
    inner_join(get_sentiments("bing")) %>% 
    mutate(method = "Bing et al."),
  pride_prejudice %>% 
    inner_join(get_sentiments("nrc") %>% 
                 filter(sentiment %in% c("positive",
                                         "negative"))) %>% 
    mutate(method = "NRC")) %>% 
    count(method, index = linenumber %/% 80, sentiment) %>% 
    spread(sentiment, n, fill = 0) %>% 
    mutate(sentiment = positive - negative)

Joining, by = "word"
Joining, by = "word"

bing_and_nrc

Comparing three sentiment lexicons using Pride and Prejudice

bind_rows(afinn,
          bing_and_nrc) %>% 
  ggplot(aes(index, sentiment, fill = method)) +
    geom_col(show.legend = FALSE) +
    facet_wrap(~method, ncol = 1 , scales = "free_y")

get_sentiments("nrc") %>% 
  filter(sentiment %in% c("positive",
                          "negative")) %>% 
  count(sentiment)

get_sentiments("bing") %>% 
  count(sentiment)

bing_words_counts <- tidy_books %>% 
  inner_join(get_sentiments("bing")) %>% 
  count(word, sentiment, sort = TRUE) %>% 
  ungroup()

Joining, by = "word"

bing_words_counts

bing_words_counts %>% 
  group_by(sentiment) %>% 
  top_n(10) %>% 
  ungroup() %>% 
  mutate(word = reorder(word, n)) %>% 
  ggplot(aes(word, n, fill = sentiment)) +
  geom_col(show.legend = FALSE)+
  facet_wrap(~sentiment, scales = "free_y") +
  labs(y = "Contribution to sentiment",
       x = NULL,
       title = "Top 10 words by Sentiment for Pride and Prejudice") +
  coord_flip()

Selecting by n

Miss is referred to as a negative word but is not in Jane Austen’s works.

custom_stop_words <- bind_rows(data_frame(word = c("miss"),
                                          lexicon = c("custom")),
                               stop_words)
custom_stop_words

Wordclouds

library(wordcloud)

Loading required package: RColorBrewer

tidy_books %>% 
  anti_join(stop_words) %>% 
  count(word) %>% 
  with(wordcloud(word, n, max.words = 100))

Joining, by = "word"

library(reshape2)

Attaching package: ‘reshape2’

The following object is masked from ‘package:tidyr’:

    smiths

tidy_books %>% 
  inner_join(get_sentiments("bing")) %>% 
  count(word, sentiment, sort = TRUE) %>% 
  acast(word~sentiment, value.car = "n", fill = 0) %>% 
  comparison.cloud(colors = c("gray20", "gray80"),
                   max.words = 100)

Joining, by = "word"
Using n as value column: use value.var to override.

Looking at Units Beyond Just Words

bingnegative <- get_sentiments("bing") %>% 
  filter(sentiment == "negative")
wordcounts <- tidy_books %>% 
  group_by(book, chapter) %>% 
  summarize(words = n())
tidy_books %>% 
  semi_join(bingnegative) %>% 
  group_by(book, chapter) %>% 
  summarize(negativewords = n()) %>% 
  left_join(wordcounts, by = c("book", "chapter")) %>% 
  mutate(ratio = negativewords/words) %>% 
  filter(chapter !=0) %>% 
  top_n(1) %>% 
  ungroup()

Joining, by = "word"
Selecting by ratio

# Chapter 3

Term Frequency in Jane Austen’s Novels

book_words <- austen_books() %>% 
  unnest_tokens(word,text) %>% 
  count(book,word,sort = TRUE) %>% 
  ungroup()
total_words <- book_words %>% 
  group_by(book) %>% 
  summarize(total = sum(n))
book_words <- left_join(book_words, total_words)

Joining, by = "book"

book_words

Term frequency distribution in Jane Austen’s novels

ggplot(book_words, aes(n/total, fill = book)) +
  geom_histogram(show.legend = FALSE) +
  xlim(NA, 0.0009) +
  facet_wrap(~book, ncol = 2, scales = "free_y")

NA

Zipf’s Law

Zipf’s Law: frequency that a word appears is inversely proportional to its rank.

freq_by_rank <- book_words %>% 
  group_by(book) %>% 
  mutate(rank = row_number(),
         'term frequency' = n/total)
freq_by_rank

Graph of Zipf’s law for Jane Austen’s novels

freq_by_rank %>% 
  ggplot(aes(rank, `term frequency`, color = book)) +
  geom_line(size = 1.1, alpha = 0.8, show.legend = TRUE) +
  scale_x_log10() +
  scale_y_log10()

rank_subset <-  freq_by_rank %>% 
  filter(rank <500,
         rank >10)
lm(log10(`term frequency`) ~ log10(rank), data = rank_subset)

Call:
lm(formula = log10(`term frequency`) ~ log10(rank), data = rank_subset)

Coefficients:
(Intercept)  log10(rank)  
    -0.6226      -1.1125  

freq_by_rank %>% 
  ggplot(aes(rank, `term frequency`, color = book))+
  geom_abline(intercept = -0.62, slope = -1.1, color = "gray50", linetype = 2) +
  geom_line(size = 1.1, alpha = 0.8, show.legend = FALSE) +
  scale_x_log10() +
  scale_y_log10()

The bind_tf_idf Function

book_words <- book_words %>% 
  bind_tf_idf(word, book, n)
book_words

book_words %>% 
  select(-total) %>% 
  arrange(desc(tf_idf))

book_words %>% 
  arrange(desc(tf_idf)) %>% 
  mutate(word = factor(word, levels = rev(unique(word)))) %>% 
  group_by(book) %>% 
  top_n(15) %>% 
  ungroup() %>% 
  ggplot(aes(word, tf_idf, fill = book)) +
  geom_col(show.legend = FALSE) +
  labs(x = NULL, y = "tf-idf") +
  facet_wrap(~book, ncol = 2,scales = "free") +
  coord_flip()

Selecting by tf_idf

A corpus of Physics Text

physics <- gutenberg_download(c(37729, 14725, 13476, 5001),
                              meta_fields = "author")

physics_words <- physics %>% 
  unnest_tokens(word, text) %>% 
  count(author, word, sort = TRUE) %>% 
  ungroup()
physics_words

plot_physics %>% 
  group_by(author) %>%
  top_n(15, tf_idf) %>% 
  ungroup() %>% 
  mutate(word = reorder(word, tf_idf)) %>% 
  ggplot(aes(word, tf_idf, fill = author)) +
  geom_col(show.legend = FALSE) +
  labs(x = NULL, y = "tf-idf") +
  facet_wrap(~author, ncol = 2, scales = "free") +
  coord_flip()

NA

Isolating “eq” from Einstein’s text

physics %>% 
  filter(str_detect(text, "eq\\.")) %>% 
  select(text)

K1 was used for the coordinate system for Einstein

physics %>%
  filter(str_detect(text, "K1")) %>% 
  select(text)

NA

physics %>%
  filter(str_detect(text, "AK")) %>% 
  select(text)

mystopwords <- data_frame(word = c("eq","co","rc","ac","ak","bn",
                                   "fig", "file", "cg", "cb", "cm"))
physics_words <- anti_join(physics_words, mystopwords, by = "word")
plot_physics <- physics_words %>% 
  bind_tf_idf(word, author, n) %>% 
  arrange(desc(tf_idf)) %>% 
  mutate(word = factor(word, levels = rev(unique(word)))) %>% 
  group_by(author) %>% 
  top_n(15, tf_idf) %>% 
  ungroup() %>% 
  mutate(author = factor(author, levels = author_order))
plot_physics

ggplot(plot_physics, aes(word, tf_idf, fill = author)) +
  geom_col(show.legend = FALSE) +
  labs( x = NULL, y = "tf-idf") +
  facet_wrap(~author, ncol = 2, scales = "free") +
  coord_flip()

Chapter 4 Relationships Between Words: N-grams and Correlations